S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

Serge Gratton; Philippe Toint

S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

Serge Gratton, Philippe Toint

Namur Institute for Complex Systems

Résultats de recherche: Papier de travail

12 Téléchargements (Pure)

Résumé

A new decoder for the SIF test problems of the \cutest\ collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most CUTEst problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations.

langue originale	Anglais
Éditeur	Arxiv
Volume	2407.07812
Etat de la publication	Publié - 10 juil. 2024

Accès au document

gt28manuscrit soumis, 434 KBLicense: Non spécifié

3 Article
1 Papier de travail

OPM, a collection of Optimization Problems in Matlab
Gratton, S. & TOINT, P., 11 déc. 2021, Arxiv.
Résultats de recherche: Papier de travail

Accès ouvert
File
CUTEST: a Constrained and Unconstrained Testing Environment with safe threads
Gould, N. I. M., Orban, D. & Toint, P., 2015, Dans: Computational Optimization and Applications. 60, 3, p. 545-557 13 p.
Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

File
217 Téléchargements (Pure)
CUTEr and SIFDEC: A constrained and unconstrained testing environment, revisited
Gould, N. I. M., Orban, D. & Toint, P. L., 1 déc. 2003, Dans: ACM Transactions on Mathematical Software. 29, 4, p. 373-394 22 p.
Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

File
102 Téléchargements (Pure)

1 Terminé
1 Actif

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
Sartenaer, A. (Co-investigateur) & Toint, P. (Co-investigateur)
1/01/87 → …
Projet: Axe de recherche
CUTE: CUTE, Constrained and Unconstrained Testing Environment
Toint, P. (Responsable du Projet), Gould, N. I. M. (Co-investigateur) & Conn, A. (Co-investigateur)
1/01/90 → 1/07/00
Projet: Recherche

1 Recherche/Enseignement dans une institution externe

Institut National Polytechnique de Toulouse
Toint, P. (Chercheur visiteur)
24 juin 2024 → 29 juin 2024
Activité: Visite d'une organisation externe › Recherche/Enseignement dans une institution externe

Contient cette citation

@techreport{986d5fde6e1c4ce79c4b4e10e51e07b1,

title = "S2MPJ and CUTEst optimization problems for Matlab, Python and Julia",

abstract = "A new decoder for the SIF test problems of the \cutest\ collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most CUTEst problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations.",

keywords = "nonlinear optimization, bechmarking, CUTEst, Matlab, Python, Julia",

author = "Serge Gratton and Philippe Toint",

year = "2024",

month = jul,

day = "10",

language = "English",

volume = "2407.07812 ",

publisher = "Arxiv",

type = "WorkingPaper",

institution = "Arxiv",

}

TY - UNPB

T1 - S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

AU - Gratton, Serge

AU - Toint, Philippe

PY - 2024/7/10

Y1 - 2024/7/10

N2 - A new decoder for the SIF test problems of the \cutest\ collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most CUTEst problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations.

AB - A new decoder for the SIF test problems of the \cutest\ collection is described, which produces problem files allowing the computation of values and derivatives of the objective function and constraints of most CUTEst problems directly within ``native'' Matlab, Python or Julia, without any additional installation or interfacing with MEX files or Fortran programs. When used with Matlab, the new problem files optionally support reduced-precision computations.

KW - nonlinear optimization, bechmarking, CUTEst, Matlab, Python, Julia

M3 - Working paper

VL - 2407.07812

BT - S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

PB - Arxiv

ER -

S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

Résumé

Accès au document

Empreinte digitale

Résultat de recherche

OPM, a collection of Optimization Problems in Matlab

CUTEST: a Constrained and Unconstrained Testing Environment with safe threads

CUTEr and SIFDEC: A constrained and unconstrained testing environment, revisited

Projets

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire

CUTE: CUTE, Constrained and Unconstrained Testing Environment

Activités

Institut National Polytechnique de Toulouse

Contient cette citation